Question about the reduce function

Questions
#1

In gcn_reduce, the neighbouring features are summed up by

return {'h' : torch.sum(nodes.mailbox['msg'], dim=1)}

But why “dim=1”?
Shouldn’t we take the sum over the dimension of samples, which should be “dim=0”?

#2

Hi,

In reduce function, we automatically did degree bucketing to accelerate the program, which means node with same in degrees are bucketed together. Therefore the shape of nodes.mailbox['msg'] is (batch_size, in_degrees, feature shape). And in reduce function you should return a dict of tensors with batch_size as first dimension. Therefore if you want to sum over all neighbors, it should be done on the dimension 1. Hope this helps!

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#3

That helps a lot!
Could you also tell me what is the shape of edges.src[‘h’] supposed to be, in the message function:

def gcn_message(edges):
    # The argument is a batch of edges.
    # This computes a (batch of) message called 'msg' using the source node's feature 'h'.
    return {'msg' : edges.src['h']}

Thanks a lot!

#4

The first dimension of edges.src['h'] is the number of edges got here. You can view it as each row is one edge, and the data init is the corresponding src data.

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#5

Thanks for your quick response!

#6

Checkout our tutorial Pagerank with DGL message passing. It explains the questions you have.

#7

That helps a lot.
Thanks.